When jointly optimizing estimated camera motion and scene geometry, SLAM systems enforce them to satisfy a certain constraint. This is done by minimizing a geometric or photometric loss to ensure their consistency in the local area - the surroundings of camera poses, which can be viewed as a bundle adjustment (BA) problem[1]}.
Learning based approaches predict depth maps and ego-motion through two individual networks [2]} trained above large datasets. During the testing procedure when deployed online, there is a requirement that enforces the predictions to satisfy the local constraints. To enable local optimization, traditionally, the second-order solvers, e.g. Gauss-Newton (GN) method or Levenberg-Marquadt (LM) algorithm [3]}, are applied to optimize motion transformations and per-pixel depth maps
